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A Decision Support System for Evaluating Aquifer Storage and Recovery Feasibility during Regional Water Planning - Dissertation summaryTRANSCRIPT
A Decision Support System for Evaluating Aquifer Storage and Recovery Feasibility
during Regional Water PlanningA Dissertation Defense by
Josue De Lara Bashulto
2
1. Introductiona) Backgroundb) Research objectives
2. Deterministic framework3. Incorporation of supply side uncertainty4. Hydro-geochemistry coupling5. Water quality inference engine6. Summary and Conclusions
OUTLINE
“Not that the story need be long, but it will take a long while to make it short” –H.D. Thoreau
3
INTRODUCTION
“following the order of nature let us begin with the principles which come first”
–Aristotle, Poetics
4
Arid and semi-arid regions Climatic variability Increased population
Water demand Water storage
Traditional: storage behind dams Issues
Natural flows Ecosystem stability Town displacement Large losses (EV)
No new dams Dam decommission
Introduction
0
0.5
1
1.5
2
2.5
3
3.5
4
Jan Feb Apr May Jul Sep Oct Dec
Pre
cip
itat
ion
(in
)
Month
2008
2007
0
500
1000
1500
2000
2500
3000
# D
ams
com
ple
ted
Decade
Source: Seattle Times, Sept. 17, 2011
5
ASR Technology Overview
Aquifers: traditional source of water Recently recognized as natural water
storage systems ASR: Dual purpose well
Extraction and well redevelopment Cost advantages
Injection of water into an aquifer Unconfined, confined, and semi-
confined formations Factors affecting ASR well operation
Quality of stored water (pre-treatment), quality of native groundwater, quality changes from mixing, hydraulic soil properties, radius of well influence, operating cycle
Page(s): 1-7,10
• Growing interest in MUS systems -> ASR• Inclusion into regional water resources
portfolio• Proved technology, however…
• Current systems are designed and operated in an ad-hoc manner
• Need for a better planning tool
6
Operational policy Optimal injection & extraction Based on medium to long-term objectives
Supply and demand Supply: highly variable, dependent on precipitation Demand: less uncertain, estimated from municipal water usage
trends Water quality
Difficult incorporation of water quality models into optimization routines due to high non-linearity and large computational requirements
Decision Support System
Need of a tool for water managers for selecting appropriate operational policies that includes the stochastic nature of precipitation in addition to water quality constraints which are often difficult to incorporate in traditional planning tools.
Page(s): 1-7
7
What fundamental concepts are needed to model and incorporate ASR systems into a DSS framework? What role does supply play on the
DSS and how can it be incorporated into water planning endeavors?
What changes does water undergo while stored on the subsurface, what is their impact on ASR operations?
How can risks related to hydro-geochemical reactions be accounted for and minimized?
Dissertation Organization
Deterministic DSS Framework
Stochastic DSS Framework
Hydro-geochemical Risks
WQ Inference Engine
Page(s): 8-9
8
DETERMINISTIC FRAMEWORK
Deterministic DSS Framework
Stochastic DSS Framework
Hydro-geochemical Risks
WQ Inference Engine?
What fundamental concepts, factors, and policies are needed to incorporate ASR technology into a regional DSS
9
Introduction: Decision Support System
Pillars Conservation principles
Simulation Operations research
Optimization General framework
Regional needs Local operational policies Scientific rigor Unduly complex Poor data availability
10
Decision Support System (DSS) Inter-dependent modules Keystone: simulation-optimization
DSS Structure
Conceptual Model
Ground Water Flow Model
Optimization Model
Operating Policy
Policy Regulations
Post-optimalityAnalysis
Page(s): 15-16
11
Conceptual Model
Page(s): 12
12
Potentiometric water level changes Injection: increased potentiometric levels (recovery) Extraction: drawdown
Confined aquifer Drawdowns respond linearly to injection/extraction operations January: In general, for a one year injection and extraction operation at
kth month:
Response weights are obtained from MODFLOW simulation with constant injection/extraction profiles. ASR1yr.FOR
Groundwater Flow Model
𝐷𝐷 𝑗𝑎𝑛=𝜀 𝑗𝑎𝑛 𝐼𝑛𝑗𝑒𝑐𝑡𝑖𝑜𝑛 𝑗𝑎𝑛+𝛿 𝑗𝑎𝑛𝐸𝑥𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑗𝑎𝑛
𝐷𝐷𝑘,𝑛=∑𝑖=1
𝑘
(𝜀𝑖 ,𝑘,𝑛𝑄𝑖𝑛𝑗 ,𝑖+𝛿𝑖 ,𝑘 ,𝑛𝑄𝑒𝑥𝑡 , 𝑖 )
Page(s): 16-18
13
Simulation-Optimization Simulation Optimization
Maximize net injection
Subject to the following constraints
Maximize net injection (t=1 to 12)𝑀𝑎𝑥∑𝑡=1
𝑇
( 𝐼 𝑡−𝐸𝑡 )
Constraint Description
Operations constraint subject to supply
Maximum drawdown limit
Maximum recovery limit
Maximum drawdown limit (of overlying formation)
Maximum recovery limit (of overlying formation)
Extraction constraint subject to comprehensive storage
Pump capacity constraint
Non-negative constraints
Page(s): 1-7
14
Case Study: CCASRCD• Corpus Christi ASR Conservation District• Seasonal & long term
storage• Water supply
• Choke Canyon Reservoir
• Lake Corpus Christi• Lake Texana
• Hydro-geologic formation• Gulf Coast Aquifer• Chicot• Evangeline
Page(s): 20-22
15
Deterministic supplies Optimized operation subject to
operational constraints Drawdowns (≤ 5ft) measured at
observation well (10,000 ft. from ASR well)
Results: Base Case Scenario
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
20
40
60
80
100
120
140
Injection
Extraction
Acr
e-ft
/mon
th
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-4
-3
-2
-1
0
1
2
3
4
Dra
wd
own
(F
t.)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
50
100
150
200
250
300
350
400
450
Supply
Demand
Acr
e-ft
/mon
th
INJECTION
STORAGE
EXTRACTION
Supply and Demand
Injection & Extraction Drawdowns
Page(s): 22-25
Deterministic framework Base structure of the Decision Support System Underlying assumptions
Isotropic hydraulic conductivity Known supplies and demand Natural gradient
What about water quality? Sensitivity analysis…
16
Sensitivity: hydraulic conductivity
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-3
-2
-1
0
1
2
3
Dra
wd
own
(F
t.)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-3
-2
-1
0
1
2
3
Dra
wd
own
(F
t.)
Random hydraulic conductivity fields
B
D
Drawdown: Case B
Drawdown: Case D
Variability in hydraulic conductivity may result in preferential flow paths. Accounting for this variability is essential to prevent loss of stored water.
Page(s): 25-27
17
20% water supply increase from base case
Year long operation Net recovery at the end of
year
Sensitivity: supply increase
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
50
100
150
200
250
300
350
400
450SupplyDemand
Acr
e-ft
/mon
th
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
20
40
60
80
100
120
140
160
180
200
InjectionExtraction
Acr
e-ft
/mon
th
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-4
-3
-2
-1
0
1
2
3
4
Dra
wd
own
(F
t.)
Supply and Demand
Injection & Extraction Drawdowns
Page(s): 29-31
18
20% water supply decrease from base case
Two months of operation Simulation-optimization
bounded by supplies No net storage at the end of
one year cycle
Sensitivity: supply decrease
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
50
100
150
200
250
300
350
400
450Supply
Demand
Acr
e-ft
/mon
th
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
10
20
30
40
50
60
70
Injection
Extraction
Acr
e-ft
/mon
th
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-4
-3
-2
-1
0
1
2
3
4
Dra
wd
own
(F
t.)
Supply and Demand
Injection & Extraction Drawdowns
Page(s): 27-29
19
Chloride concentration Native: 1000 mg/l chloride Source: 155 mg/ l chloride
Water quality modeling MT3D
Water quality affected by Mixing Supplies
Sensitivity: water quality
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
100
200
300
400
500
600
700
800
900
1000
Ch
lori
de
con
cen
trat
ion
(m
g/l)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
100
200
300
400
500
600
700
800
900
1000
Ch
lori
de
con
cen
trat
ion
(m
g/l)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
100
200
300
400
500
600
700
800
900
1000
Ch
lori
de
con
cen
trat
ion
(m
g/l)
Base Case
20% Supply Reduction20% Supply Increase
Page(s): 33-36
20
Deterministic approach to introduce fundamental concepts of ASR operations
Simulation-optimization One year cycle of operations
Sensitivity Hydraulic conductivity:
preferential paths and land requirements Variability in supply
ASR feasibility, project sizing, water quality considerations Small changes have large implications in terms of water
quality and ASR operations
Summary: Deterministic Framework
Page(s): 37-38
21
INCORPORATION OF SUPPLY SIDE
UNCERTAINTYDeterministic DSS
Framework
Stochastic DSS Framework
Hydro-geochemical Risks
WQ Inference Engine?How can variability in supplies
be accounted for and included into a decision support system
22
Supplies Precipitation erratic
Simulation-modeling Traditional: Monte Carlo Inter-annual variability
Month to month correlation ~ 0.5 Intra-annual variability (2001-2009)
Markov Chain Able to capture sequential month-to-month variability Introduction of Markov Chain – Monte Carlo method
Supply Variability
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecCorrel -0.040 -0.232 0.087 -0.259 -0.327 -0.270 -0.158 -0.469 0.577 0.697 0.146 -0.070
Page(s): 39-43
MAY JUNE
23
DSS Flow-chart
Conceptual Model
Ground Water Flow Model
Optimization Model
Operating Policy
Policy Regulations
Supply
Call MC-MCI
i=i+1
i=0
i≤n Yes
No
STOP
Page(s): 43-46
24
Building the MC-MCI Gather historic supplies: probabilistic analysis
Characterization
Transition probabilities
Applying the MC-MCI
WATER SUPPLY
Page(s): 43-46
Historic Supplies JanuaryMin Max
LOW MED HIGHJANUARY
LOW MED HIGHFEBRUARY
α β γ Low Med HighLow 0.63 0.38 0.00Med 0.00 1.00 0.00High 0.00 0.00 1.00
TRANSITION: Jan-Feb
25
10 year water supplies Choke Canyon Reservoir Lake Corpus Christi Lake Texana
Statistic foundation MC-MCI FRAMEWORK
Case Study: CCASRCD
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecAverage 5994 5605 6464 7231 7206 7689 8267 8259 6690 6664 6282 6090Max 6903 6437 7495 10290 7926 9490 13294 9693 7111 7296 6983 7709Min 5646 5144 5887 5796 5796 6640 6818 7338 6101 6176 5967 4657Range 1257 1293 1608 4494 2130 2850 6476 2355 1010 1120 1016 3052StdDev 381.8 376.9 572.3 1289.3 673.7 873.7 2086.2 773.1 374.0 348.0 325.9 799.9COV 0.064 0.067 0.089 0.178 0.093 0.114 0.252 0.094 0.056 0.052 0.052 0.131
Page(s): 47-49
26
Stochastic water supply 10 year diversion data (2001 to 2010) at Nueces River and Mary Rhodes
pipeline monthly supply characterization 100 realizations of annual water supply sequences
Monte-Carlo & Markov Chain Iteration (MC-MCI) Ground water flow (MODFLOW) & quality (MT3D) model
100 x 100 squared cells (500 ft. in length) Two layered system
Unconfined (Chicot formation) Confined (Evangeline formation)
Modeled after the Gulf Coast Aquifer
VBA subroutines MC-MCI Simulation-Optimization
Modeling Parameters
Page(s): 47-49
Water table965 ft
Potentiometricsurface800 ft
Confining layer500 ft
Unconfinedformation
Confinedformation
TOP VIEW SIDE VIEW
ASR
10000 ft
50000 ft
5000
0 ft
500
ft50
0 ft
27
Results: long-term ASR operations
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
50
100
150
200
250
300
350
400
450Supply
Demand
Acr
e-ft
/mon
th
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0.00
50.00
100.00
150.00
200.00
Acr
e-ft
/mon
th
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0.00
50.00
100.00
150.00
200.00
Acr
e-ft
/mon
th
Supply and Demand
Injection Profile Extraction Profile
Page(s): 50-54
Supply and demand Long-term mean operational profile
Injections January - May, September
Extractions April - December
28
Drawdowns Constraint limits
Exceedance Water managers Operations estimate
Water quality Long term improvement
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
Dra
wd
own
( f
t)
Results: long-term ASR operations
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
100
200
300
400
500
600
700
800
900
1000
Ch
lori
de
con
cen
trat
ion
(m
g/L
)
Drawdowns
Water Quality
Page(s): 50-54
275
278
282
285
289
292
296
299
303
306
310
313
317
320
324
327
331
334
338
341
345
0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%
100.00%
Acre-ft/month
Exceedance
Exceedance February
29
Challenge: Addressing the temporal disconnect
Solution: Store water at times when it is available Use during periods of high demand
Variability in supply Use stochastic supply distribution long term mean supply MC-MCI approach
Capture inter-annual variability Long-term ASR operational policy
Reliability: 35%
Summary: Supply Side Uncertainty
Page(s): 55
Recap: supply played a huge role in operations and water quality (mixing)
How to incorporate water quality into the DSS? Next chapter…
30
HYDRO-GEOCHEMISTRY COUPLING
Deterministic DSS Framework
Stochastic DSS Framework
Hydro-geochemical Risks
WQ Inference Engine?What changes does water
undergo while in storage and what are their impacts to ASR operations
31
Long-term operational policy Water supply availability, storage, and extraction
Water available for extraction Injected water and native groundwater
Thermodynamic equilibrium Dissolution and precipitation
Health and operational concerns DBPs, radioactive, carcinogens (i.e. As, U)
Including water quality into the DSS Water quality is not a single parameter Multimedia reactions (liquid-liquid, liquid-solid, liquid-gas)
Introduction
Understand potential implications of water quality alterations
How much mixing occurs? What are the risks of toxic mineral dissolution? Facilitate the assessment of ASR systems: water quality
parameters characterization
ASR PROJECT OBJECTIVES
IDENTIFY WATER QUALITY (WQ) NEEDS(E.G. INTENDED USE, REGULATIONS)
IS WQ DATA AVAILABLE
GATHER RELEVANT DATA FROM SURROUNDING REGION
AND/OR LITERATURE STUDIES
WQ DATA INTERPOLATION(E.G. KRIGING)GEOCHEMICAL ANALYSIS
MIXING QUANTIFICATION EQUILIBRIUM REACTION MODELING (E.G. PHREEQC)
IDENTIFY POTENTIAL DISSOLUTION/PRECIPITATION USING SATURATION INDICES
IDENTIFY CHANGES IN CONCENTRATION OF
CONSTITUENTS OF INTEREST
IS ASR PROJECT VIABLE CONSIDERING ADDITIONAL COSTS AND INCREASED RISKS CONCERNING WATER
QUALITY?
SOLUTE TRANSPORT MODEL (ADVECTION & DISPERSION)
MIXING FRACTION, EQ(6)
NO
YES
32Page(s): 72-73
33
Parameters of concern for ASR projects Water supply source: reclaimed water vs. surplus fresh water
Regulations & operations research experience Compiled list: parameters of interest
Identification of WQ parameters
General WQ ParametersAlkalinity, DO, pH, redox potential, specific conductance, TDS, TSS, temperature, TC, turbidity, and total hardness
Major ionsCalcium, potassium, sodium, magnesium, chloride, bicarbonate, sulfate, and nitrate
Minor, trace, and otherInorganics (As), radionuclides (U), DBPs (THMs and HAAs)
Page(s): 58-64 Full list and description on Tables 7 and 8, pages 60 & 64, and Appendix A
34
Law of mass-action
Solubility of mineral AB
,
Solubility due to mixing
SI < -0.05 Mineral is undersaturated, indicating dissolution-0.05 ≤ SI ≤0.05 Mineral is in equilibrium with solution
SI > 0.05 Mineral is oversaturated, indicating possible precipitation
Page(s): 65-67
Recovery Efficiency
35
Thermodynamic Equilibrium Packages (Table 10, pg. 70)
Data availability Lack of consistent data in many regions (e.g. CCASRCD)
Water quality data interpolation Use available data at nearby locations Robust geo-spatial interpolation Kriging
Geochemical Modeling & Data Gaps
WATEQ4F MINTEQA2 EQ3NR/EQ6 PHREEQC
Determine data quality and availabilityFirst• Preliminary data analysis, descriptive statistics, data gaps
Variance modelingSecond• Semivariogram fitting and control parameters
Map generationThird• Extraction of water quality at points of interest
Page(s): 70-80
36
Case Study: CCASRCD
Page(s): 73-77
37
Mixing quantification Chloride: conservative tracer
Native water: ~ 1,000 mg/L Injected water: 155 mg/l Long-term ASR mean operational policy
Results: mixing fraction (Xi)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%NativeInjected
Water quality Mixing fraction
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecInjection 65.31 130.43 64.46 1.94 32.44 0.04 0.00 0.00 3.07 0.00 0.00 0.00Extraction 0.00 0.00 0.00 22.57 0.00 18.66 139.73 60.24 1.48 10.87 8.35 13.35
𝐗𝐢=𝐕 𝐢
𝐕 𝐫=𝐂𝐧−𝐂𝐫
𝐂𝐧−𝐂𝐢
Mixing fraction
Page(s): 73-77
38
Major ions in extracted water 50% mixing fractions (Fig.42, pg. 82)
Source + native Changes (mmeq/L)
Results: Major Ions
Comparison (mmeq / L)
Mixing fraction Xi = 50%
Page(s): 82-84
Injected Native Mix 50%Ca2+ 2.465 2.300 2.216Mg2+ 0.667 1.135 0.907Na+ 4.828 33.033 23.940K+ 0.003 0.144 0.058CO3
2- 2.418 0.087 0.042HCO3
- 2.426 4.949 2.749Cl- 4.936 26.179 20.290SO4
2- 1.978 5.076 3.390
Graph
39
Saturation Index Higher potential for dissolution Uranium species Mineralogy study
Saturation Indices
U3As4
U3S5
U2C3
U2S3
Orpiment
-1000 -500 0
Element Xi=20 Xi=30 Xi=40 Xi=50
Al 0.99 1.48 1.97 2.47 As 0.08 0.07 0.06 0.05 Ba 0.13 0.20 0.26 0.33 C 4937.00 4320.00 3703.00 3086.00 Ca 1220.00 1222.00 1223.00 1225.00 Cl 8451.00 8012.00 7573.00 7134.00 Cu 0.00 0.00 0.01 0.01 F 51.21 51.13 51.05 50.96 K 193.50 169.30 145.20 121.00 Mg 643.20 604.40 565.70 527.00 N 22.89 22.53 22.17 21.80 Na 11940.00 11050.00 10160.00 9272.00 Pb 0.00 0.01 0.01 0.01 S 818.00 839.50 860.90 882.30 Si 403.20 352.80 302.40 252.00 Sr 8.23 7.20 6.17 5.14 U 0.00 0.00 0.00 0.00
Saturation Indices Selected concentrations (mmolal)
Page(s): 84
40
ASR systems: water supply quality Differing water quality: native and supply sources
Subsurface interactions Thermodynamic equilibrium disruption Multimedia and multivariate reactions
Reduction-oxidation reactions, adsorption, ion-exchange, and dissolution-precipitation of species.
Concern of increased health and operational risks Framework for assessing changes in water quality
1. Define constituents of interest2. Evaluation metrics (mixing fraction, and saturation index)3. Methodology for addressing data poor conditions4. Hydro-geochemical analysis (loosely coupled)
Summary
Page(s): 86
41
WATER QUALITY INFERENCE ENGINE
Deterministic DSS Framework
Stochastic DSS Framework
Hydro-geochemical Risks
WQ Inference Engine?How can risks related to
hydro-geochemical reactions be accounted for and minimized?
42
Assessing water quality alterations Loosely coupled
Tightly integrated water quality evaluation framework Water quality decisions fully integrated into the DSS Integral part of the simulation-optimization routine
Challenges Numerical solutions to advection-dispersion PDE Water quality of extracted water non-linear response
Introduction
ASR operational framework (quantity)
Hydro-geochemistry coupling (quality)
Page(s): 87-88
43
Water Quality Model
Optimization Model
Operating Policy
Policy Regulations Supply
Call MC-MCI
i=i+1
i=0
i≤N?
STOP
Yes
No
METHODOLOGY
ASR Long-term operational policy
Page(s): 89-90
Conceptual Model
Ground Water Flow Model
44
Simulation-Optimization Simulation Optimization
Maximize net injection
Subject to the following constraints
Maximize net injection (t=1 to 12)𝑀𝑎𝑥∑𝑡=1
𝑇
( 𝐼 𝑡−𝐸𝑡 )
Constraint Description
Operations constraint subject to supply
Water quality constraint
Maximum drawdown limit
Maximum recovery limit
Extraction constraint subject to comprehensive storage
Pump capacity constraint
Maximum drawdown limit (of overlying formation)
Maximum recovery limit (of overlying formation)
Non-negative constraintsPage(s): 89-90
45
Water Quality: Artificial Neural Network
Generate random one year inj./ext. profile
Injection
Extraction
Water flow model: MODFLOW
Water quality model: MT3D Concentration
j=0
j=j+i
j ≤K
STOP
NO
Yes
ArtificialNeural
Network
Training:Minimize residual
errorANN Weights
VALIDATION
1
2
3
Bias
A
B
C
O
Input Layer Hidden Layer Output Layer
Weights
Weights
Bias
Page(s): 91-97
46
CCASRCD Hypothetical well
Based on Kriging maps (Appendix B, pg. 126-142)
Chloride TWDB database MCL 300 mg/l
Case study
47
860 880 900 920 940 960 980 1000800
850
900
950
1000f(x) = 0.999998869997668 xR² = 0.999999999484492
Observed Concentration (mg/L)
Pre
dict
ed C
once
ntra
tion
(m
g/L
)
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecInput 2 4 6 8 10 12 14 16 18 20 22 24Hidden nodes 3 3 3 5 5 5 5 5 5 5 5 5Output 1 1 1 1 1 1 1 1 1 1 1 1
ANN structure Training & Validation Mixing Fraction (30-40%)
High mixing in single cycle – operational alternative
Results: ANN PERFORMANCEGOODNESS OF FIT
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecSSD 0.21 0.17 1.11 0.95 3.77 1.81 3.10 3.44 5.42 4.92 8.02 10.22R >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecSSD 0.10 0.06 0.64 0.64 1.99 2.01 1.80 2.13 4.47 4.03 6.24 10.01R >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99
ANN STRUCTURE
Validation (200 yrs.)
Page(s): 100-104
Training (400 yrs.)
48
Investment Continuous WQ
improvement Build up of buffer zone
One-time investment Economically sound
Results: Water Investment
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
100
200
300
400
500
600
700
800
900
1000
Ch
lori
de
con
cen
trat
ion
(m
g/L
)
Investment Scenarios
Water qualityPost 2 yr. Investment
BUFFER
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecInvestment 65 130 64 0 32 0 0 0 1 0 0 0Injection(+), Extraction(-) 65 130 64 -21 32 -19 -140 -61 1 -11 -9 -14
Page(s): 104-108
ASR feasibility Hydrogeochemical reactions
Increased risks: health and operational Water quality DSS
Computational load System non-linearities
Water quality inference engine Artificial Neural network
Abstract non-linear responses Tight integration simulation-optimization Quick evaluation of water availability of a given quality Maintain recovery efficiencies Meet federal and state water quality regulations
49
Summary
WQ inference
engine
Native &
source WQ
Supply and
demand
Page(s): 104-108
50
SUMMARY AND CONCLUSIONS
Deterministic DSS Framework
Stochastic DSS Framework
Hydro-geochemical Risks
WQ Inference Engine
51
Water scarcity in arid and semi-arid regions Need to store water when available to use at times of
increased demand Water storage alternatives
Surface reservoirs and managed underground storage (MUS) Traditional vs. evolving perspective
Aquifers = potential water storage infrastructure MUS technologies: SAT & ASR ASR systems: dual purpose well system
Lack of a tool to assist water managers: project feasibility Development of a decision support system (DSS)
Summary and Conclusions
52
Summary and Conclusions
Deterministic frameworkChapter 2
•Introduction of fundamental concepts of ASR operations•Development of DSS structure powered by a simulation-optimization•Case study: Corpus Christi ASR Conservation District (CCASRCD)•Obtained operational supply based on known water demand and supply•Sensitivity analysis: supply, hydraulic conductivity, hydraulic gradient, water quality
Incorporation of supply uncertaintyChapter 3
•Importance of characterizing water supply variability project feasibility•Enhanced DSS to capture regional water supply variability•Introduction of Markov Chain – Monte Carlo iteration (MC-MCI) method•Case study: Obtained long-term mean ASR operational policy•Project feasibility and long term reliability (~35%)
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Summary and Conclusions
Water quality considerations of Aquifer Storage and Recovery operations
Chapter 4
• Importance of identifying water quality changes during storage• Loosely coupled hydro-geochemical evaluation framework for
DSS• Parameter categorization, addressing data gaps, hydro-
geochemical reactions• Water quality evaluation metrics: saturation index (SI), and
mixing fraction (Xi)• Case study: identification of potential locations for ASR project
A long-term ASR operational policy with WQ constraints based on an ANN inference engine
Chapter 5
• Capitulating differences of coupling water quality evaluation methodologies
• Challenges of a tightly coupled water quality decision support system
• Introduction of a water quality inference engine based on ANN technology
• Case study: ANN evaluation and the importance of water investing• Tool for WQ alterations, maintain recovery efficiencies and meet
regulations
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Limitations of the presented work
Future Work
Costs1• Phase one approach: is the system able to enhance water
supplies?• Capital and operational cost estimates of current ASR projects
• Twin Oaks ASR in San Antonio (~1/6th of surface storage alternatives*) Multi-layered ASR2
• Minimal foot print of current ASR systems• Additional advantages of placing multiple wells at a single site• Storing water at different formations
Horizontal directional drilling (HDD)3
• HDD used to mine oil and natural gas• Similar approach to store water in shallow aquifers• Address horizontal hydraulic variability, greater surface area
*Pyne, R.D.G., 2010. Stacking of ASR Wells in Multiple Aquifers, National Ground Water Association 2010 Summit, Denver, CO.Page(s): 114-115
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Horizontal Directional Drilling ASR
ConfinedAquifer
UnconfinedAquiferConfining layer
Confining layer
Pump
Horizontal well
Water table965 ft
Potentiometricsurface800 ft
Confining layer500 ft
Unconfinedformation
Confinedformation
TOP VIEW SIDE VIEW
HDDASR
50000 ft
5000
0 ft
500
ft50
0 ft
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“A simulation-optimization model for ASR operational planning subject to probabilistic supply and water quality constraints” – NGWA Conference, 2011
“Water chemistry effects of Aquifer Storage and Recovery (ASR) operations” – NGWA Conference, 2011
“Técnicas de recarga artificial en zonas áridas y semiáridas” – Universidad Autonoma de Zacatecas Conference, 2010
“A Stochastic Dynamic Programming Model for Planning ASR Operations Under Uncertainty” –NGWA Conference, 2010
“Simulation-Optimization of Soil-Aquifer Treatment System Release Patterns” – Javelina Research Symposium, 2010
“Decision Support Systems (DSS) for Managed Underground Storage Technologies of Recoverable Water (MUS)” - TAMUK- EVEN Seminar Series, 2010
“A probabilistic analysis of wet and dry regimes: Bi-national study along the Rio Grande River” - TAMUK- EVEN Seminar Series, 2008
Presentations
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ACKNOWLEDGMENTS
This material is based upon work supported by the Center of Research Excellence in Science and Technology – Research on Environmental Sustainability of Semi-Arid Coastal Areas (CREST-RESSACA) at Texas A&M University– Kingsville (TAMUK) through a Cooperative Agreement (No. HRD-0734850) from the National Science Foundation (NSF). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Consejo Nacional de Ciencia y Tecnología
Department of Environmental Engineering &College of Graduate Studies
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TH NKSQUESTIONS
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